n_nodes = 50 # number of nodes
d = 3 # dimension of variable at each node
# 2. function
# objective value
np.random.seed(200)
v = np.random.rand(n_nodes, d)
# optimal value
x_opt = v.mean()
# 3. simulation setting
graphs = [
nx.path_graph(n_nodes),
nx.cycle_graph(n_nodes),
nx.lollipop_graph(n_nodes // 2, n_nodes - n_nodes // 2),
Simulator.erdos_renyi(n_nodes, 0.1)
]
graph_name = ['Line', 'Cycle', 'Lollipop', 'ER(p=0.1)']
line_style = ['-rd', '-c^', '-bs', '-go']
best_penalty = [7.5, 3.7, 5.56, 1.4]
mode = 'H-CADMM'
max_iter = 1000
# start simulation
setting = {
'penalty': -1,
'max_iter': max_iter,
'objective': v,
'initial': 0 * np.random.randn(n_nodes, d),
'epsilon': 1e-8,
# 'random_hyperedge': [],
'n_FC': -1
#%%
# 1. graph
n_nodes = 50 # number of nodes
d = 3 # dimension of variable at each node
np.random.seed(1000)
# 2. function
# objective value
v = np.random.rand(n_nodes, d)
# optimal value
x_opt = v.mean()
# 3. simulation setting
graphs = [
nx.lollipop_graph(n_nodes // 2, n_nodes - n_nodes // 2),
nx.connected_caveman_graph(n_nodes // 5, 5),
Simulator.erdos_renyi(n_nodes, 0.05, seed=501),
Simulator.erdos_renyi(n_nodes, 0.1, seed=1000)
]
graph_name = ['Lollipop', 'Caveman', 'ER(p=0.05)', 'ER(p=0.1)']
line_style = ['--rd', '-rd', '--c^', '-c^', '--bs', '-bs', '--go', '-go']
best_penalty = [{
'D-CADMM': 5,
'H-CADMM': 5.5
}, {
'D-CADMM': 1.57,
'H-CADMM': 2.45
}, {
'D-CADMM': 1.5,
'H-CADMM': 1.85
}, {
'D-CADMM': .75,
